import pymysql.cursors
import numpy as np
import pymysql
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from joblib import dump
import os

class MysqlUtils(object):
    def __init__(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            user='root',
            passwd='root',
            database='scenic',
            charset='utf8'
        )
    
    def is_holiday(self, date):
        return 0

    def get_scenic_data(self):
        cursor = self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
            SELECT DATE(g.create_time) as date, HOUR(g.create_time) as hour, COUNT(*) as count
            FROM order_user_gate_rel g
            WHERE HOUR(g.create_time) BETWEEN 6 AND 23
            GROUP BY date, hour
        """
        cursor.execute(sql)
        ret = cursor.fetchall()
        df = pd.DataFrame(ret)

        date_range = pd.date_range(start='2024-07-01', end='2025-03-01', freq='D')
        hours = range(6, 24)
        full_index = pd.MultiIndex.from_product([date_range, hours], names=['date', 'hour'])
        df_full = df.set_index(['date', 'hour']).reindex(full_index, fill_value=0).reset_index()

        df_pivot = df_full.pivot(index='date', columns='hour', values='count')

        # 确保索引是日期类型
        df_pivot.index = pd.to_datetime(df_pivot.index)

        df_pivot['dow'] = df_pivot.index.dayofweek  # 星期几（0-6）
        df_pivot['month'] = df_pivot.index.month  # 月份
        # df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        # print(df_pivot.head())

        df_pivot = pd.get_dummies(df_pivot, columns=['dow', 'month'], dtype=int)

        print(df_pivot)

        hours_columns = list(range(6, 24))
        df_hours = df_pivot[hours_columns].copy()

        feature_columns = [col for col in df_pivot.columns if col not in hours_columns]
        df_feature = df_pivot[feature_columns].copy()

        scaler = MinMaxScaler()
        scaler_hours = scaler.fit_transform(df_hours)
        
        # 确保目录存在
        os.makedirs('NN', exist_ok=True)
        dump(scaler, 'NN/scaler.joblib')

        df_hour_scaled = pd.DataFrame(scaler_hours, columns=hours_columns, index=df_hours.index)

        df_pivot_clean = pd.concat([df_hour_scaled, df_feature], axis=1)
        print(df_pivot_clean)

        df_pivot_clean.to_csv('NN/scenic_data.csv', index=False)

if __name__ == '__main__':
    mu = MysqlUtils()
    mu.get_scenic_data() 